--------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/jrthornton/Desktop/replication.log
  log type:  text
 opened on:  30 Jun 2023, 13:36:23

. do "/Users/jrthornton/Desktop/replication/Social desirability and satisfaction wtih democracy replication.do"

. use "/Users/jrthornton/Documents/Work/2. Papers/Current papers/1. Democracy paper with Hamad/anes_timeseries_cdf_s
> tata_20211118/anes_timeseries_cdf_stata_20211118.dta" , clear

. set more off

. 
. ********************************************************************************
. *                                                Coding                                            *
. ********************************************************************************
. 
. * Survey year
. gen year = VCF0004

. drop if year<2012
(49,760 observations deleted)

. drop if year>2016
(8,280 observations deleted)

. 
. gen y = 0 if year==2012
(4,270 missing values generated)

. replace y = 1 if year==2016
(4,270 real changes made)

. 
. * Weighting
. svyset [pw=VCF0009z]

      pweight: VCF0009z
          VCE: linearized
  Single unit: missing
     Strata 1: <one>
         SU 1: <observations>
        FPC 1: <zero>

. 
. * Mode
. gen mode = VCF0017

. gen internet = 0 if mode == 0
(6,950 missing values generated)

. replace internet = 1 if mode==4
(6,950 real changes made)

. 
. gen i2020 = 1 if VCF0017==4 & year==2020
(10,184 missing values generated)

. replace i2020 = 0 if VCF0017==3 & year==2020
(0 real changes made)

. replace i2020 = 0 if VCF0017==5 & year==2020
(0 real changes made)

. 
. * Satisfaction with democracy
. gen satisfaction = VCF9254 if year<2002
(10,184 missing values generated)

. replace satisfaction = . if satisfaction<0
(0 real changes made)

. replace satisfaction = VCF9255 if year>2002
(10,184 real changes made)

. replace satisfaction = . if satisfaction<0
(1,131 real changes made, 1,131 to missing)

. revrs satisfaction , replace

. 
. * Binary satisfaction
. gen satis_binary = satisfaction
(1,131 missing values generated)

. recode satis_binary (1=0) (2=0) (3=1) (4=1)
(satis_binary: 9053 changes made)

. 
. * Winner
. gen vote = VCF0713

. recode vote (1=0) (2=1) (3=.) (4=.) (9=.) (0=.)
(vote: 10184 changes made)

. gen winner=vote
(2,906 missing values generated)

. recode winner (1=0) (0=1) if year==2008
(winner: 0 changes made)

. recode winner (1=0) (0=1) if year==2012
(winner: 4351 changes made)

. recode winner (1=0) (0=1) if year==1996
(winner: 0 changes made)

. recode winner (1=0) (0=1) if year==2020
(winner: 0 changes made)

. label define winner 0 "Loser" 1 "Winner" , replace

. label values winner winner 

. 
. * Partisanship and party
. gen pid = VCF0301

. recode pid (0=.)
(pid: 47 changes made)

. replace pid = . if pid==9
(0 real changes made)

. 
. gen party = pid
(47 missing values generated)

. recode party (1=1) (2=1) (3=1) (5=0) (6=0) (7=0) (0=.) (4=2)
(party: 7762 changes made)

. label define partylabel 0 "Republican" 1 "Democrat"  2 "Independent" , replace

. label values party partylabel 

. 
. gen repdem = party if party<2
(1,418 missing values generated)

. label values repdem partylabel 

. 
. * Ideology 
.         
. * Self ideology (recoded to range -3-3; -2 Havent thought, -8 DK, -9 NA)
. gen ideo = VCF0803

. replace ideo = . if ideo < 1 
(663 real changes made, 663 to missing)

. replace ideo = . if ideo == 9 
(1,172 real changes made, 1,172 to missing)

. 
. * Education
. gen education = VCF0110

. replace edu = . if educ==0
(107 real changes made, 107 to missing)

. 
. * Race
. gen race = VCF0105b

. recode race (0=.) (9=.)
(race: 57 changes made)

. label define racelabel 1 "White" 2 "Black" 3 "Hispanic" 4 "Other"  , replace

. label values race racelabel  

. *1. White non-Hispanic
. *2. Black non-Hispanic
. *3. Hispanic
. *4. Other or multiple races, non-Hispanic
. gen black = 0 if race==1
(3,628 missing values generated)

. 
. * Age
. gen age = VCF0101

. replace age = 97 if age>97
(0 real changes made)

. replace age = . if age==0
(181 real changes made, 181 to missing)

. 
. * Sex (female)
. gen female = VCF0104

. replace female = . if fem ==0
(41 real changes made, 41 to missing)

. replace female = . if fem ==3
(11 real changes made, 11 to missing)

. replace female = fem-1
(10,132 real changes made)

. label define femalelabel 0 "Male" 1 "Female" , replace

. label values female femalelabel  

. 
. * Income
. gen income = VCF0114

. replace income = . if income == 0
(312 real changes made, 312 to missing)

. 
. * Region 
. gen region = VCF0112

. label define region 1 "Northeast" 2 "Midwest" 3 "South" 4 "West" , replace

. label values region region  

. 
. * Marital status
. gen married =  VCF0147 if  VCF0147 < 8
(34 missing values generated)

. recode married (7=6)
(married: 904 changes made)

. label define marriedlabel 1 "Married" 2 "Never married" 3 "Divorced" ///
>         4 "Separated" 5 "Widowed" 6 "Partners" , replace 

. label values married marriedlabel  

. 
. * Interest
. gen interest = VCF0310

. recode interest (9=.) (0=.)
(interest: 5 changes made)

. 
. * Economic perceptions
. gen natl_econ = VCF0871*-1+6

. 
. * Alaska/Hawaii
. gen ALHI = 1 if VCF0901a == 15
(10,168 missing values generated)

. replace ALHI = 1 if VCF0901a == 2
(7 real changes made)

. replace ALHI = 0 if ALHI!=1
(10,161 real changes made)

. 
. ********************************************************************************
. *                                   Analyses                                   *
. ********************************************************************************
. 
. *****************
. * Main analyses *
. *****************
. svy: reg satis_binary internet age income female i.married i.region i.race ///
>         edu if year == 2012
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      5,183
Number of PSUs     =     5,183                  Population size   = 5,162.9384
                                                Design df         =      5,182
                                                F(  16,   5167)   =      12.69
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0559

--------------------------------------------------------------------------------
               |             Linearized
  satis_binary |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.1646109   .0180653    -9.11   0.000    -.2000265   -.1291954
           age |   .0020009    .000635     3.15   0.002      .000756    .0032458
        income |   .0263413   .0088956     2.96   0.003     .0089021    .0437805
        female |   .0315524   .0176536     1.79   0.074     -.003056    .0661608
               |
       married |
Never married  |   .0643649    .026378     2.44   0.015     .0126529     .116077
     Divorced  |   .0225693   .0278253     0.81   0.417    -.0319801    .0771187
    Separated  |   .0168808   .0527971     0.32   0.749    -.0866238    .1203854
      Widowed  |   .0556886   .0371495     1.50   0.134      -.01714    .1285172
     Partners  |   .0153007   .0327745     0.47   0.641    -.0489512    .0795527
               |
        region |
      Midwest  |  -.0164169   .0273428    -0.60   0.548    -.0700203    .0371865
        South  |  -.0333257   .0254842    -1.31   0.191    -.0832856    .0166342
         West  |  -.0114935   .0278637    -0.41   0.680    -.0661181    .0431311
               |
          race |
        Black  |   .1640308   .0267711     6.13   0.000     .1115482    .2165135
     Hispanic  |   .1862226   .0263608     7.06   0.000     .1345442     .237901
        Other  |   .0284616   .0391289     0.73   0.467    -.0482476    .1051707
               |
     education |   .0288572   .0110233     2.62   0.009     .0072469    .0504674
         _cons |   .4529097   .0604028     7.50   0.000     .3344947    .5713247
--------------------------------------------------------------------------------

. svy: reg satis_binary internet age income female i.married i.region i.race ///
>         edu if year == 2016
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      3,413
Number of PSUs     =     3,413                  Population size   =   3,445.93
                                                Design df         =      3,412
                                                F(  16,   3397)   =       8.15
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0544

--------------------------------------------------------------------------------
               |             Linearized
  satis_binary |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.0754119   .0208988    -3.61   0.000    -.1163873   -.0344366
           age |   .0034502   .0006266     5.51   0.000     .0022215    .0046788
        income |   .0494372   .0104107     4.75   0.000     .0290254     .069849
        female |  -.0042516   .0192001    -0.22   0.825    -.0418965    .0333933
               |
       married |
Never married  |  -.0276822   .0307904    -0.90   0.369    -.0880517    .0326874
     Divorced  |    .000683   .0316859     0.02   0.983    -.0614422    .0628081
    Separated  |   .0049777   .0739754     0.07   0.946     -.140063    .1500183
      Widowed  |   .0241874   .0442783     0.55   0.585    -.0626273    .1110022
     Partners  |  -.0735878   .0343988    -2.14   0.032    -.1410322   -.0061434
               |
        region |
      Midwest  |  -.0156097   .0305852    -0.51   0.610    -.0755769    .0443575
        South  |   .0014503   .0279761     0.05   0.959    -.0534013    .0563019
         West  |  -.0813461   .0315372    -2.58   0.010    -.1431797   -.0195125
               |
          race |
        Black  |  -.0229814    .035296    -0.65   0.515    -.0921849     .046222
     Hispanic  |   .0325637   .0340387     0.96   0.339    -.0341747     .099302
        Other  |  -.0310156   .0371917    -0.83   0.404    -.1039359    .0419047
               |
     education |   .0036138   .0124942     0.29   0.772     -.020883    .0281106
         _cons |   .4346473   .0689948     6.30   0.000     .2993719    .5699226
--------------------------------------------------------------------------------

. svy: reg satis_binary internet age income female i.married i.region i.race ///
>         edu i.y 
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      8,596
Number of PSUs     =     8,596                  Population size   = 8,608.8684
                                                Design df         =      8,595
                                                F(  17,   8579)   =      12.26
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0396

--------------------------------------------------------------------------------
               |             Linearized
  satis_binary |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.1300808   .0139282    -9.34   0.000    -.1573834   -.1027783
           age |   .0026097   .0004616     5.65   0.000     .0017049    .0035144
        income |   .0360731   .0068697     5.25   0.000     .0226068    .0495393
        female |   .0161349   .0132258     1.22   0.223    -.0097908    .0420605
               |
       married |
Never married  |   .0246186   .0203437     1.21   0.226    -.0152598    .0644971
     Divorced  |   .0122999   .0212063     0.58   0.562    -.0292695    .0538694
    Separated  |   .0108415   .0438864     0.25   0.805    -.0751865    .0968694
      Widowed  |   .0397659   .0286541     1.39   0.165     -.016403    .0959347
     Partners  |   -.023435   .0242376    -0.97   0.334    -.0709466    .0240765
               |
        region |
      Midwest  |  -.0149126   .0205996    -0.72   0.469    -.0552927    .0254675
        South  |  -.0194595   .0190315    -1.02   0.307    -.0567659    .0178469
         West  |   -.039752   .0210299    -1.89   0.059    -.0809756    .0014715
               |
          race |
        Black  |   .0922711   .0217042     4.25   0.000     .0497257    .1348164
     Hispanic  |   .1249867   .0212653     5.88   0.000     .0833017    .1666718
        Other  |   .0055729   .0273156     0.20   0.838    -.0479722    .0591181
               |
     education |   .0193731   .0084356     2.30   0.022     .0028374    .0359088
           1.y |   .0105959   .0130986     0.81   0.419    -.0150805    .0362724
         _cons |    .439104   .0460432     9.54   0.000     .3488482    .5293598
--------------------------------------------------------------------------------

.         
. ****************
. * Balance test *
. ****************
. svy: logit internet age income female i.married i.region i.race edu ///
>         if year == 2012
(running logit on estimation sample)

Survey: Logistic regression

Number of strata   =         1                  Number of obs     =      5,594
Number of PSUs     =     5,594                  Population size   = 5,578.9303
                                                Design df         =      5,593
                                                F(  15,   5579)   =       0.68
                                                Prob > F          =     0.8032

--------------------------------------------------------------------------------
               |             Linearized
      internet |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
           age |   .0025056   .0027504     0.91   0.362    -.0028863    .0078974
        income |   .0123199   .0407607     0.30   0.762    -.0675869    .0922267
        female |  -.0028266   .0785709    -0.04   0.971     -.156856    .1512029
               |
       married |
Never married  |    .192984    .112401     1.72   0.086    -.0273655    .4133336
     Divorced  |   .1074697   .1255259     0.86   0.392    -.1386098    .3535493
    Separated  |   -.202775   .2548308    -0.80   0.426    -.7023423    .2967922
      Widowed  |  -.0402332   .1746274    -0.23   0.818    -.3825708    .3021044
     Partners  |  -.1661676   .1367879    -1.21   0.225     -.434325    .1019898
               |
        region |
      Midwest  |  -.0012257   .1256728    -0.01   0.992    -.2475932    .2451418
        South  |   .0370524   .1157728     0.32   0.749    -.1899073     .264012
         West  |   .1001323   .1285864     0.78   0.436     -.151947    .3522116
               |
          race |
        Black  |   .0240645   .1159626     0.21   0.836    -.2032672    .2513962
     Hispanic  |   .0522871   .1161958     0.45   0.653    -.1755018     .280076
        Other  |  -.1467862   .1658749    -0.88   0.376    -.4719655     .178393
               |
     education |   .0061146   .0501412     0.12   0.903    -.0921815    .1044108
         _cons |   .4447993   .2556232     1.74   0.082    -.0563214    .9459199
--------------------------------------------------------------------------------

. svy: logit internet age income female i.married i.region i.race edu ///
>         if year == 2016
(running logit on estimation sample)

Survey: Logistic regression

Number of strata   =         1                  Number of obs     =      3,997
Number of PSUs     =     3,997                  Population size   =  4,035.802
                                                Design df         =      3,996
                                                F(  15,   3982)   =       0.40
                                                Prob > F          =     0.9800

--------------------------------------------------------------------------------
               |             Linearized
      internet |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
           age |  -.0037157   .0030491    -1.22   0.223    -.0096936    .0022623
        income |   .0465244   .0452544     1.03   0.304    -.0421995    .1352483
        female |  -.0150773    .087398    -0.17   0.863    -.1864261    .1562716
               |
       married |
Never married  |  -.1023761   .1378584    -0.74   0.458    -.3726554    .1679033
     Divorced  |   .1037508   .1355696     0.77   0.444    -.1620412    .3695429
    Separated  |  -.1522116   .2912066    -0.52   0.601    -.7231389    .4187157
      Widowed  |   .1782966   .1875829     0.95   0.342    -.1894706    .5460638
     Partners  |   .0016853   .1500517     0.01   0.991    -.2924997    .2958702
               |
        region |
      Midwest  |  -.0098841   .1445148    -0.07   0.945    -.2932137    .2734455
        South  |  -.0235575   .1322444    -0.18   0.859    -.2828303    .2357154
         West  |   .0002222   .1483856     0.00   0.999    -.2906963    .2911407
               |
          race |
        Black  |  -.0712497   .1562572    -0.46   0.648    -.3776009    .2351016
     Hispanic  |   .0182163   .1412678     0.13   0.897    -.2587474    .2951801
        Other  |   .0050545   .1613375     0.03   0.975     -.311257    .3213659
               |
     education |  -.0731436   .0563376    -1.30   0.194    -.1835968    .0373096
         _cons |   1.329524   .2911091     4.57   0.000     .7587879     1.90026
--------------------------------------------------------------------------------

. 
. 
. ***********************
. * Logistic regression *
. ***********************
.         
. svy: logit satis_binary internet age income female i.married i.region i.race ///
>         edu if year == 2012
(running logit on estimation sample)

Survey: Logistic regression

Number of strata   =         1                  Number of obs     =      5,183
Number of PSUs     =     5,183                  Population size   = 5,162.9384
                                                Design df         =      5,182
                                                F(  16,   5167)   =      10.05
                                                Prob > F          =     0.0000

--------------------------------------------------------------------------------
               |             Linearized
  satis_binary |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.7964712   .0946732    -8.41   0.000    -.9820706   -.6108718
           age |    .009245   .0029699     3.11   0.002     .0034227    .0150674
        income |   .1211916   .0418454     2.90   0.004      .039157    .2032262
        female |   .1488638    .082922     1.80   0.073    -.0136984    .3114259
               |
       married |
Never married  |   .2993659   .1242295     2.41   0.016     .0558237     .542908
     Divorced  |    .105857   .1309146     0.81   0.419    -.1507909    .3625049
    Separated  |   .0876131   .2667978     0.33   0.743    -.4354231    .6106493
      Widowed  |   .2789765   .1888352     1.48   0.140    -.0912201    .6491731
     Partners  |   .0649778    .153922     0.42   0.673    -.2367742    .3667299
               |
        region |
      Midwest  |  -.0826972   .1282459    -0.64   0.519    -.3341133     .168719
        South  |  -.1574148   .1208855    -1.30   0.193    -.3944014    .0795719
         West  |   -.056616   .1325781    -0.43   0.669    -.3165249     .203293
               |
          race |
        Black  |   .7962837    .144807     5.50   0.000     .5124009    1.080166
     Hispanic  |   .9166465   .1443764     6.35   0.000      .633608    1.199685
        Other  |   .1232814   .1786925     0.69   0.490    -.2270313    .4735942
               |
     education |   .1383106   .0520984     2.65   0.008     .0361757    .2404454
         _cons |  -.2439741   .2827375    -0.86   0.388    -.7982589    .3103108
--------------------------------------------------------------------------------

. margins , at(internet=(0 1)) post

Predictive margins                              Number of obs     =      5,183
Model VCE    : Linearized

Expression   : Pr(satis_binary), predict()

1._at        : internet        =           0

2._at        : internet        =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .765882   .0143699    53.30   0.000      .737711     .794053
          2  |   .6016164     .01089    55.24   0.000     .5802674    .6229654
------------------------------------------------------------------------------

. mlincom 2-1

             |   lincom    pvalue        ll        ul 
-------------+----------------------------------------
           1 |   -0.164     0.000    -0.200    -0.129 

. 
. svy: logit satis_binary internet age income female i.married i.region i.race ///
>         edu if year == 2016
(running logit on estimation sample)

Survey: Logistic regression

Number of strata   =         1                  Number of obs     =      3,413
Number of PSUs     =     3,413                  Population size   =   3,445.93
                                                Design df         =      3,412
                                                F(  16,   3397)   =       7.37
                                                Prob > F          =     0.0000

--------------------------------------------------------------------------------
               |             Linearized
  satis_binary |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.3611409   .1042611    -3.46   0.001    -.5655614   -.1567204
           age |   .0161681   .0030128     5.37   0.000     .0102609    .0220752
        income |   .2240338   .0479299     4.67   0.000     .1300597     .318008
        female |  -.0204222   .0905755    -0.23   0.822    -.1980098    .1571654
               |
       married |
Never married  |  -.1106316   .1361957    -0.81   0.417    -.3776651    .1564019
     Divorced  |  -.0092344   .1515499    -0.06   0.951    -.3063723    .2879034
    Separated  |   .0144244   .3324011     0.04   0.965    -.6373008    .6661497
      Widowed  |   .1248385   .2365154     0.53   0.598    -.3388876    .5885646
     Partners  |  -.3160741   .1497055    -2.11   0.035    -.6095956   -.0225525
               |
        region |
      Midwest  |  -.0792714   .1495581    -0.53   0.596     -.372504    .2139612
        South  |   .0008718   .1386031     0.01   0.995    -.2708816    .2726253
         West  |  -.3778607   .1501873    -2.52   0.012    -.6723268   -.0833946
               |
          race |
        Black  |  -.1049512   .1571175    -0.67   0.504    -.4130051    .2031026
     Hispanic  |   .1520096   .1600571     0.95   0.342    -.1618078     .465827
        Other  |   -.143829   .1677837    -0.86   0.391    -.4727957    .1851377
               |
     education |   .0183493   .0593784     0.31   0.757    -.0980714      .13477
         _cons |  -.3350864   .3211547    -1.04   0.297    -.9647614    .2945885
--------------------------------------------------------------------------------

. margins , at(internet=(0 1)) post

Predictive margins                              Number of obs     =      3,413
Model VCE    : Linearized

Expression   : Pr(satis_binary), predict()

1._at        : internet        =           0

2._at        : internet        =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .7148405     .01761    40.59   0.000     .6803133    .7493677
          2  |   .6400654   .0112791    56.75   0.000     .6179508    .6621799
------------------------------------------------------------------------------

. mlincom 2-1

             |   lincom    pvalue        ll        ul 
-------------+----------------------------------------
           1 |   -0.075     0.000    -0.116    -0.034 

. 
. svy: logit satis_binary internet age income female i.married i.region i.race ///
>         edu i.y 
(running logit on estimation sample)

Survey: Logistic regression

Number of strata   =         1                  Number of obs     =      8,596
Number of PSUs     =     8,596                  Population size   = 8,608.8684
                                                Design df         =      8,595
                                                F(  17,   8579)   =      10.56
                                                Prob > F          =     0.0000

--------------------------------------------------------------------------------
               |             Linearized
  satis_binary |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.6221418   .0709496    -8.77   0.000    -.7612201   -.4830635
           age |   .0119988   .0021477     5.59   0.000     .0077888    .0162088
        income |   .1639083   .0315811     5.19   0.000     .1020017    .2258148
        female |   .0749645   .0610794     1.23   0.220    -.0447657    .1946947
               |
       married |
Never married  |   .1153088   .0922373     1.25   0.211    -.0654985    .2961161
     Divorced  |   .0563509    .099187     0.57   0.570    -.1380794    .2507812
    Separated  |   .0562278   .2092883     0.27   0.788    -.3540274     .466483
      Widowed  |   .2024378   .1462131     1.38   0.166    -.0841749    .4890506
     Partners  |  -.1005464   .1077894    -0.93   0.351    -.3118395    .1107467
               |
        region |
      Midwest  |  -.0748897     .09688    -0.77   0.440    -.2647977    .1150183
        South  |  -.0953413   .0904291    -1.05   0.292    -.2726039    .0819214
         West  |  -.1877736   .0986115    -1.90   0.057    -.3810758    .0055287
               |
          race |
        Black  |    .428462   .1053832     4.07   0.000     .2218856    .6350384
     Hispanic  |   .5912275    .107025     5.52   0.000     .3814329    .8010221
        Other  |   .0249232   .1225504     0.20   0.839    -.2153049    .2651514
               |
     education |   .0925524   .0391593     2.36   0.018     .0157907    .1693141
           1.y |   .0510682     .06066     0.84   0.400    -.0678401    .1699764
         _cons |  -.3099537   .2121024    -1.46   0.144    -.7257252    .1058179
--------------------------------------------------------------------------------

. margins , at(internet=(0 1)) post

Predictive margins                              Number of obs     =      8,596
Model VCE    : Linearized

Expression   : Pr(satis_binary), predict()

1._at        : internet        =           0

2._at        : internet        =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .7480562   .0113371    65.98   0.000     .7258328    .7702796
          2  |   .6179603   .0079718    77.52   0.000     .6023336     .633587
------------------------------------------------------------------------------

. mlincom 2-1

             |   lincom    pvalue        ll        ul 
-------------+----------------------------------------
           1 |   -0.130     0.000    -0.157    -0.103 

. 
. 
. *****************************
. * Without Alaska and Hawaii *
. *****************************
. svy: reg satis_binary internet age income female i.married i.region i.race ///
>         edu if year == 2012 & ALHI==0
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      5,174
Number of PSUs     =     5,174                  Population size   = 5,156.1695
                                                Design df         =      5,173
                                                F(  16,   5158)   =      12.67
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0560

--------------------------------------------------------------------------------
               |             Linearized
  satis_binary |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.1645689   .0180752    -9.10   0.000     -.200004   -.1291338
           age |   .0020403   .0006356     3.21   0.001     .0007943    .0032864
        income |   .0263611    .008903     2.96   0.003     .0089075    .0438147
        female |    .030507   .0176688     1.73   0.084    -.0041312    .0651453
               |
       married |
Never married  |   .0652039   .0263986     2.47   0.014     .0134514    .1169564
     Divorced  |    .023717   .0278431     0.85   0.394    -.0308673    .0783013
    Separated  |   .0170366   .0528331     0.32   0.747    -.0865386    .1206119
      Widowed  |   .0547964   .0372036     1.47   0.141    -.0181383    .1277312
     Partners  |   .0153043   .0328135     0.47   0.641     -.049024    .0796326
               |
        region |
      Midwest  |  -.0162817   .0273438    -0.60   0.552    -.0698872    .0373237
        South  |  -.0332414    .025484    -1.30   0.192    -.0832009    .0167181
         West  |  -.0111149   .0279146    -0.40   0.691    -.0658394    .0436095
               |
          race |
        Black  |   .1641016   .0267715     6.13   0.000     .1116183     .216585
     Hispanic  |    .186407   .0263792     7.07   0.000     .1346926    .2381215
        Other  |   .0283512   .0393281     0.72   0.471    -.0487484    .1054508
               |
     education |   .0290914   .0110387     2.64   0.008     .0074509    .0507318
         _cons |   .4504662   .0604398     7.45   0.000     .3319787    .5689537
--------------------------------------------------------------------------------

. svy: reg satis_binary internet age income female i.married i.region i.race ///
>         edu if year == 2016 & ALHI==0
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      3,404
Number of PSUs     =     3,404                  Population size   =  3,438.796
                                                Design df         =      3,403
                                                F(  16,   3388)   =       8.07
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0542

--------------------------------------------------------------------------------
               |             Linearized
  satis_binary |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.0752141   .0209108    -3.60   0.000    -.1162131   -.0342151
           age |   .0034581   .0006275     5.51   0.000     .0022279    .0046883
        income |    .048942   .0104386     4.69   0.000     .0284755    .0694085
        female |  -.0041046   .0192295    -0.21   0.831    -.0418072     .033598
               |
       married |
Never married  |  -.0278143   .0308616    -0.90   0.368    -.0883234    .0326948
     Divorced  |   .0012251   .0317284     0.04   0.969    -.0609835    .0634336
    Separated  |   .0006137   .0749541     0.01   0.993     -.146346    .1475734
      Widowed  |   .0235311   .0443092     0.53   0.595    -.0633441    .1104064
     Partners  |   -.073911   .0344071    -2.15   0.032    -.1413717   -.0064504
               |
        region |
      Midwest  |  -.0156755   .0305842    -0.51   0.608    -.0756408    .0442897
        South  |   .0013315   .0279724     0.05   0.962    -.0535129     .056176
         West  |  -.0809806   .0316352    -2.56   0.011    -.1430066   -.0189546
               |
          race |
        Black  |  -.0228477    .035294    -0.65   0.517    -.0920474    .0463519
     Hispanic  |   .0326851   .0340477     0.96   0.337     -.034071    .0994412
        Other  |  -.0278995   .0375404    -0.74   0.457    -.1015035    .0457044
               |
     education |   .0038749   .0125074     0.31   0.757    -.0206479    .0283976
         _cons |   .4347207   .0691281     6.29   0.000     .2991839    .5702574
--------------------------------------------------------------------------------

. svy: reg satis_binary internet age income female i.married i.region i.race ///
>         edu i.y if ALHI==0
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      8,578
Number of PSUs     =     8,578                  Population size   = 8,594.9655
                                                Design df         =      8,577
                                                F(  17,   8561)   =      12.24
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0396

--------------------------------------------------------------------------------
               |             Linearized
  satis_binary |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.1299841   .0139351    -9.33   0.000    -.1573003   -.1026679
           age |   .0026377    .000462     5.71   0.000      .001732    .0035434
        income |   .0358771     .00688     5.21   0.000     .0223906    .0493636
        female |    .015571   .0132405     1.18   0.240    -.0103835    .0415255
               |
       married |
Never married  |   .0251104   .0203714     1.23   0.218    -.0148226    .0650433
     Divorced  |   .0132895   .0212238     0.63   0.531    -.0283142    .0548933
    Separated  |   .0091891   .0441012     0.21   0.835      -.07726    .0956381
      Widowed  |   .0389701   .0286845     1.36   0.174    -.0172584    .0951986
     Partners  |  -.0235289   .0242545    -0.97   0.332    -.0710735    .0240158
               |
        region |
      Midwest  |  -.0148823   .0205998    -0.72   0.470    -.0552629    .0254983
        South  |  -.0194623   .0190309    -1.02   0.306    -.0567674    .0178428
         West  |  -.0394248   .0210783    -1.87   0.061    -.0807433    .0018937
               |
          race |
        Black  |   .0923709   .0217062     4.26   0.000     .0498216    .1349203
     Hispanic  |   .1251152   .0212741     5.88   0.000     .0834127    .1668176
        Other  |   .0067551   .0275028     0.25   0.806    -.0471569    .0606672
               |
     education |    .019607    .008446     2.32   0.020     .0030508    .0361631
           1.y |   .0106667   .0131138     0.81   0.416    -.0150395    .0363729
         _cons |   .4376474   .0460979     9.49   0.000     .3472843    .5280104
--------------------------------------------------------------------------------

. 
. **************
. * Unweighted *
. **************
. * Unweighted replication of main anlyses
. reg satis_binary internet age income female i.married i.region i.race edu ///
>         if year == 2012

      Source |       SS           df       MS      Number of obs   =     5,183
-------------+----------------------------------   F(16, 5166)     =     20.01
       Model |  65.2860582        16  4.08037864   Prob > F        =    0.0000
    Residual |  1053.57676     5,166  .203944398   R-squared       =    0.0584
-------------+----------------------------------   Adj R-squared   =    0.0554
       Total |  1118.86282     5,182  .215913319   Root MSE        =     .4516

--------------------------------------------------------------------------------
  satis_binary |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.1525457   .0139814   -10.91   0.000    -.1799552   -.1251362
           age |    .001598    .000452     3.54   0.000     .0007119     .002484
        income |   .0147502   .0064228     2.30   0.022     .0021588    .0273415
        female |   .0235317    .012788     1.84   0.066    -.0015383    .0486016
               |
       married |
Never married  |   .0223247   .0188727     1.18   0.237    -.0146738    .0593231
     Divorced  |   -.006232   .0205261    -0.30   0.761    -.0464718    .0340077
    Separated  |  -.0099549   .0403819    -0.25   0.805    -.0891205    .0692108
      Widowed  |   .0186518   .0287992     0.65   0.517    -.0378069    .0751105
     Partners  |   -.024093   .0241204    -1.00   0.318    -.0713791    .0231932
               |
        region |
      Midwest  |  -.0119515   .0206227    -0.58   0.562    -.0523808    .0284778
        South  |  -.0209081    .018722    -1.12   0.264    -.0576111    .0157949
         West  |  -.0134022    .020767    -0.65   0.519    -.0541143    .0273098
               |
          race |
        Black  |   .1723932   .0182678     9.44   0.000     .1365806    .2082059
     Hispanic  |   .1612899   .0188292     8.57   0.000     .1243767    .1982031
        Other  |  -.0059903   .0279746    -0.21   0.830    -.0608322    .0488517
               |
     education |   .0256245   .0080951     3.17   0.002     .0097546    .0414944
         _cons |   .5332182   .0424697    12.56   0.000     .4499597    .6164768
--------------------------------------------------------------------------------

. reg satis_binary internet age income female i.married i.region i.race edu ///
>         if year == 2016

      Source |       SS           df       MS      Number of obs   =     3,413
-------------+----------------------------------   F(16, 3396)     =     11.62
       Model |  39.2089399        16  2.45055874   Prob > F        =    0.0000
    Residual |  716.324608     3,396  .210931863   R-squared       =    0.0519
-------------+----------------------------------   Adj R-squared   =    0.0474
       Total |  755.533548     3,412  .221434217   Root MSE        =    .45927

--------------------------------------------------------------------------------
  satis_binary |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |   -.070818   .0174707    -4.05   0.000    -.1050722   -.0365638
           age |   .0034253   .0005384     6.36   0.000     .0023697     .004481
        income |   .0464902   .0082505     5.63   0.000     .0303137    .0626667
        female |  -.0089829   .0161118    -0.56   0.577    -.0405727    .0226068
               |
       married |
Never married  |  -.0258648   .0241311    -1.07   0.284    -.0731776    .0214481
     Divorced  |   .0035866   .0254635     0.14   0.888    -.0463386    .0535119
    Separated  |  -.0332076   .0575238    -0.58   0.564    -.1459924    .0795772
      Widowed  |   .0373414   .0359876     1.04   0.300    -.0332181    .1079008
     Partners  |  -.0643268   .0292317    -2.20   0.028    -.1216404   -.0070132
               |
        region |
      Midwest  |   .0031184   .0253583     0.12   0.902    -.0466008    .0528376
        South  |   .0168038   .0235848     0.71   0.476     -.029438    .0630455
         West  |  -.0910366   .0260089    -3.50   0.000    -.1420313   -.0400418
               |
          race |
        Black  |   -.030479   .0280819    -1.09   0.278    -.0855381    .0245801
     Hispanic  |   .0457627   .0275096     1.66   0.096    -.0081744    .0996997
        Other  |   -.041053   .0303809    -1.35   0.177    -.1006197    .0185137
               |
     education |   .0043246   .0106003     0.41   0.683     -.016459    .0251082
         _cons |   .4282306   .0554572     7.72   0.000     .3194978    .5369634
--------------------------------------------------------------------------------

. reg satis_binary internet age income female i.married i.region i.race edu i.y

      Source |       SS           df       MS      Number of obs   =     8,596
-------------+----------------------------------   F(17, 8578)     =     21.10
       Model |  75.2609341        17  4.42711377   Prob > F        =    0.0000
    Residual |  1799.63192     8,578  .209796214   R-squared       =    0.0401
-------------+----------------------------------   Adj R-squared   =    0.0382
       Total |  1874.89286     8,595  .218137622   Root MSE        =    .45804

--------------------------------------------------------------------------------
  satis_binary |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.1322993   .0109037   -12.13   0.000    -.1536732   -.1109254
           age |   .0022977   .0003471     6.62   0.000     .0016173    .0029781
        income |   .0264604   .0050982     5.19   0.000     .0164668    .0364541
        female |   .0111345   .0100671     1.11   0.269    -.0085995    .0308685
               |
       married |
Never married  |   .0013362   .0149653     0.09   0.929    -.0279993    .0306717
     Divorced  |  -.0027875   .0160825    -0.17   0.862     -.034313     .028738
    Separated  |  -.0197407    .033311    -0.59   0.553    -.0850383    .0455569
      Widowed  |   .0229502   .0226349     1.01   0.311    -.0214196      .06732
     Partners  |  -.0417414   .0187259    -2.23   0.026    -.0784486   -.0050342
               |
        region |
      Midwest  |  -.0101868   .0160889    -0.63   0.527    -.0417248    .0213513
        South  |  -.0090147   .0147538    -0.61   0.541    -.0379356    .0199062
         West  |  -.0453145   .0163142    -2.78   0.005    -.0772944   -.0133347
               |
          race |
        Black  |   .1116437   .0153546     7.27   0.000     .0815449    .1417425
     Hispanic  |   .1272324   .0155849     8.16   0.000     .0966823    .1577824
        Other  |  -.0183166   .0206968    -0.88   0.376    -.0588874    .0222542
               |
     education |   .0177595    .006469     2.75   0.006     .0050787    .0304404
           1.y |   .0003251   .0102874     0.03   0.975    -.0198407     .020491
         _cons |   .5067753   .0339255    14.94   0.000     .4402732    .5732773
--------------------------------------------------------------------------------

. 
. * Unweighted balance test
. logit internet age income female i.married i.region i.race edu if year == 2012

Iteration 0:   log likelihood = -3570.5128  
Iteration 1:   log likelihood =  -3296.137  
Iteration 2:   log likelihood = -3292.5126  
Iteration 3:   log likelihood =  -3292.509  
Iteration 4:   log likelihood =  -3292.509  

Logistic regression                             Number of obs     =      5,594
                                                LR chi2(15)       =     556.01
                                                Prob > chi2       =     0.0000
Log likelihood =  -3292.509                     Pseudo R2         =     0.0779

--------------------------------------------------------------------------------
      internet |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
           age |   .0244393   .0021654    11.29   0.000     .0201952    .0286833
        income |   .0916644   .0301361     3.04   0.002     .0325987    .1507301
        female |  -.2524568   .0606627    -4.16   0.000    -.3713536     -.13356
               |
       married |
Never married  |  -.0362698   .0882012    -0.41   0.681     -.209141    .1366015
     Divorced  |  -.3656319   .0958907    -3.81   0.000    -.5535743   -.1776896
    Separated  |  -.6461961   .1794392    -3.60   0.000    -.9978904   -.2945018
      Widowed  |  -.5937186    .135618    -4.38   0.000    -.8595249   -.3279122
     Partners  |  -.2509885   .1100327    -2.28   0.023    -.4666486   -.0353284
               |
        region |
      Midwest  |   .0962098   .1008303     0.95   0.340    -.1014139    .2938335
        South  |  -.0435542   .0897095    -0.49   0.627    -.2193817    .1322733
         West  |  -.0006251   .0998854    -0.01   0.995    -.1963968    .1951466
               |
          race |
        Black  |  -.7838192   .0817962    -9.58   0.000    -.9441369   -.6235015
     Hispanic  |  -.5883281   .0842271    -6.99   0.000    -.7534101    -.423246
        Other  |  -.5154871   .1271851    -4.05   0.000    -.7647654   -.2662088
               |
     education |   .2135013   .0385083     5.54   0.000     .1380264    .2889762
         _cons |  -.8142947   .1983659    -4.11   0.000    -1.203085   -.4255047
--------------------------------------------------------------------------------

. logit internet age income female i.married i.region i.race edu if year == 2016

Iteration 0:   log likelihood = -2365.1445  
Iteration 1:   log likelihood = -2341.4872  
Iteration 2:   log likelihood = -2341.3669  
Iteration 3:   log likelihood = -2341.3669  

Logistic regression                             Number of obs     =      3,997
                                                LR chi2(15)       =      47.56
                                                Prob > chi2       =     0.0000
Log likelihood = -2341.3669                     Pseudo R2         =     0.0101

--------------------------------------------------------------------------------
      internet |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
           age |  -.0004939   .0024377    -0.20   0.839    -.0052717     .004284
        income |   .0307787   .0368952     0.83   0.404    -.0415346    .1030921
        female |      .0633   .0727645     0.87   0.384    -.0793158    .2059158
               |
       married |
Never married  |  -.0258592   .1097913    -0.24   0.814    -.2410462    .1893277
     Divorced  |  -.1361716   .1140648    -1.19   0.233    -.3597346    .0873914
    Separated  |  -.4001589   .2374175    -1.69   0.092    -.8654887    .0651708
      Widowed  |   -.376537   .1547028    -2.43   0.015    -.6797489   -.0733252
     Partners  |  -.0151532   .1312622    -0.12   0.908    -.2724224     .242116
               |
        region |
      Midwest  |  -.1757333   .1184899    -1.48   0.138    -.4079693    .0565026
        South  |  -.2745843   .1094836    -2.51   0.012    -.4891681   -.0600005
         West  |  -.1286017   .1212478    -1.06   0.289    -.3662431    .1090396
               |
          race |
        Black  |  -.0987376   .1239879    -0.80   0.426    -.3417494    .1442742
     Hispanic  |  -.4265457   .1160316    -3.68   0.000    -.6539633    -.199128
        Other  |  -.0369461   .1346482    -0.27   0.784    -.3008518    .2269596
               |
     education |   .0860815   .0473954     1.82   0.069    -.0068117    .1789747
         _cons |   .8835772   .2430722     3.64   0.000     .4071645     1.35999
--------------------------------------------------------------------------------

. 
. * Figures E1 and E2
. logit internet age income female i.married i.region i.race edu if year == 2016

Iteration 0:   log likelihood = -2365.1445  
Iteration 1:   log likelihood = -2341.4872  
Iteration 2:   log likelihood = -2341.3669  
Iteration 3:   log likelihood = -2341.3669  

Logistic regression                             Number of obs     =      3,997
                                                LR chi2(15)       =      47.56
                                                Prob > chi2       =     0.0000
Log likelihood = -2341.3669                     Pseudo R2         =     0.0101

--------------------------------------------------------------------------------
      internet |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
           age |  -.0004939   .0024377    -0.20   0.839    -.0052717     .004284
        income |   .0307787   .0368952     0.83   0.404    -.0415346    .1030921
        female |      .0633   .0727645     0.87   0.384    -.0793158    .2059158
               |
       married |
Never married  |  -.0258592   .1097913    -0.24   0.814    -.2410462    .1893277
     Divorced  |  -.1361716   .1140648    -1.19   0.233    -.3597346    .0873914
    Separated  |  -.4001589   .2374175    -1.69   0.092    -.8654887    .0651708
      Widowed  |   -.376537   .1547028    -2.43   0.015    -.6797489   -.0733252
     Partners  |  -.0151532   .1312622    -0.12   0.908    -.2724224     .242116
               |
        region |
      Midwest  |  -.1757333   .1184899    -1.48   0.138    -.4079693    .0565026
        South  |  -.2745843   .1094836    -2.51   0.012    -.4891681   -.0600005
         West  |  -.1286017   .1212478    -1.06   0.289    -.3662431    .1090396
               |
          race |
        Black  |  -.0987376   .1239879    -0.80   0.426    -.3417494    .1442742
     Hispanic  |  -.4265457   .1160316    -3.68   0.000    -.6539633    -.199128
        Other  |  -.0369461   .1346482    -0.27   0.784    -.3008518    .2269596
               |
     education |   .0860815   .0473954     1.82   0.069    -.0068117    .1789747
         _cons |   .8835772   .2430722     3.64   0.000     .4071645     1.35999
--------------------------------------------------------------------------------

. margins , at(age=(17 20(10)90))

Predictive margins                              Number of obs     =      3,997
Model VCE    : OIM

Expression   : Pr(internet), predict()

1._at        : age             =          17

2._at        : age             =          20

3._at        : age             =          30

4._at        : age             =          40

5._at        : age             =          50

6._at        : age             =          60

7._at        : age             =          70

8._at        : age             =          80

9._at        : age             =          90

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .7244571   .0170807    42.41   0.000     .6909794    .7579347
          2  |   .7241648   .0157865    45.87   0.000     .6932239    .7551057
          3  |   .7231891   .0116973    61.83   0.000     .7002629    .7461154
          4  |   .7222114   .0083755    86.23   0.000     .7057957    .7386271
          5  |   .7212315   .0070557   102.22   0.000     .7074026    .7350605
          6  |   .7202496   .0087366    82.44   0.000     .7031261    .7373731
          7  |   .7192655   .0122633    58.65   0.000     .6952299    .7433012
          8  |   .7182794   .0165095    43.51   0.000     .6859215    .7506373
          9  |   .7172912   .0210583    34.06   0.000     .6760178    .7585646
------------------------------------------------------------------------------

. margins , at(married=(1(1)6)) post

Predictive margins                              Number of obs     =      3,997
Model VCE    : OIM

Expression   : Pr(internet), predict()

1._at        : married         =           1

2._at        : married         =           2

3._at        : married         =           3

4._at        : married         =           4

5._at        : married         =           5

6._at        : married         =           6

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .7330834   .0104513    70.14   0.000     .7125992    .7535676
          2  |   .7280371   .0177888    40.93   0.000     .6931716    .7629025
          3  |   .7058567   .0205042    34.42   0.000     .6656692    .7460443
          4  |   .6488117   .0517708    12.53   0.000     .5473428    .7502805
          5  |   .6541236    .032499    20.13   0.000     .5904268    .7178203
          6  |   .7301335   .0228484    31.96   0.000     .6853514    .7749156
------------------------------------------------------------------------------

. # delimit ;
delimiter now ;
.         coefplot  
>         ,
>         vert 
>         msymbol(O) mcolor(black) lcolor(black)
>         aspect(1) 
>         xlab(,angle(90)) 
>         ylab(.5 "0.5" .6 "0.6" .7 "0.7") 
>         xlab(1 "Married" 
>                 2 "Never married" 
>                 3 "Divorced" 
>                 4 "Separated" 
>                 5 "Widowed" 
>                 6 "Partners")
>         xtitle(" ") 
>         ytitle("Pr(Internet)")
>         ;

. # delimit cr
delimiter now cr
. 
. logit internet age income female i.married i.region i.race edu if year == 2016

Iteration 0:   log likelihood = -2365.1445  
Iteration 1:   log likelihood = -2341.4872  
Iteration 2:   log likelihood = -2341.3669  
Iteration 3:   log likelihood = -2341.3669  

Logistic regression                             Number of obs     =      3,997
                                                LR chi2(15)       =      47.56
                                                Prob > chi2       =     0.0000
Log likelihood = -2341.3669                     Pseudo R2         =     0.0101

--------------------------------------------------------------------------------
      internet |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
           age |  -.0004939   .0024377    -0.20   0.839    -.0052717     .004284
        income |   .0307787   .0368952     0.83   0.404    -.0415346    .1030921
        female |      .0633   .0727645     0.87   0.384    -.0793158    .2059158
               |
       married |
Never married  |  -.0258592   .1097913    -0.24   0.814    -.2410462    .1893277
     Divorced  |  -.1361716   .1140648    -1.19   0.233    -.3597346    .0873914
    Separated  |  -.4001589   .2374175    -1.69   0.092    -.8654887    .0651708
      Widowed  |   -.376537   .1547028    -2.43   0.015    -.6797489   -.0733252
     Partners  |  -.0151532   .1312622    -0.12   0.908    -.2724224     .242116
               |
        region |
      Midwest  |  -.1757333   .1184899    -1.48   0.138    -.4079693    .0565026
        South  |  -.2745843   .1094836    -2.51   0.012    -.4891681   -.0600005
         West  |  -.1286017   .1212478    -1.06   0.289    -.3662431    .1090396
               |
          race |
        Black  |  -.0987376   .1239879    -0.80   0.426    -.3417494    .1442742
     Hispanic  |  -.4265457   .1160316    -3.68   0.000    -.6539633    -.199128
        Other  |  -.0369461   .1346482    -0.27   0.784    -.3008518    .2269596
               |
     education |   .0860815   .0473954     1.82   0.069    -.0068117    .1789747
         _cons |   .8835772   .2430722     3.64   0.000     .4071645     1.35999
--------------------------------------------------------------------------------

. 
. 
. ***********
. * Ordinal *
. ***********
. svy: reg satisfaction internet age income female i.married i.region i.race ///
>         edu if year == 2012
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      5,183
Number of PSUs     =     5,183                  Population size   = 5,162.9384
                                                Design df         =      5,182
                                                F(  16,   5167)   =      11.68
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0527

--------------------------------------------------------------------------------
               |             Linearized
  satisfaction |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.2444327   .0312965    -7.81   0.000     -.305787   -.1830784
           age |   .0031478   .0010144     3.10   0.002     .0011593    .0051364
        income |   .0280815   .0145852     1.93   0.054    -.0005117    .0566746
        female |   .0097826   .0286359     0.34   0.733    -.0463558    .0659211
               |
       married |
Never married  |   .0731456   .0408024     1.79   0.073    -.0068443    .1531355
     Divorced  |   .0431935   .0475622     0.91   0.364    -.0500484    .1364355
    Separated  |   .1148898   .1057921     1.09   0.278    -.0925073     .322287
      Widowed  |   .1386473   .0642674     2.16   0.031     .0126561    .2646385
     Partners  |   .0508454   .0515282     0.99   0.324    -.0501716    .1518623
               |
        region |
      Midwest  |   -.044216   .0447913    -0.99   0.324    -.1320259    .0435939
        South  |  -.0805917   .0436257    -1.85   0.065    -.1661164    .0049331
         West  |   -.050268   .0466332    -1.08   0.281    -.1416887    .0411528
               |
          race |
        Black  |   .3062125   .0430952     7.11   0.000     .2217278    .3906972
     Hispanic  |   .2740747   .0429559     6.38   0.000     .1898631    .3582863
        Other  |   .0083191   .0705268     0.12   0.906    -.1299432    .1465814
               |
     education |   .0390707   .0182416     2.14   0.032     .0033094     .074832
         _cons |   2.458487   .0982914    25.01   0.000     2.265794    2.651179
--------------------------------------------------------------------------------

. svy: reg satisfaction internet age income female i.married i.region i.race ///
>         edu if year == 2016
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      3,413
Number of PSUs     =     3,413                  Population size   =   3,445.93
                                                Design df         =      3,412
                                                F(  16,   3397)   =       8.59
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0590

--------------------------------------------------------------------------------
               |             Linearized
  satisfaction |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.1298568    .034001    -3.82   0.000    -.1965212   -.0631923
           age |   .0063607   .0010544     6.03   0.000     .0042933    .0084282
        income |   .0763505     .01714     4.45   0.000     .0427448    .1099562
        female |  -.0306843   .0299747    -1.02   0.306    -.0894544    .0280858
               |
       married |
Never married  |  -.0199487   .0488541    -0.41   0.683     -.115735    .0758377
     Divorced  |  -.0483677   .0551508    -0.88   0.381    -.1564997    .0597644
    Separated  |   .1593531   .1243492     1.28   0.200    -.0844534    .4031597
      Widowed  |  -.0077142   .0694972    -0.11   0.912    -.1439745    .1285462
     Partners  |  -.0851803    .054193    -1.57   0.116    -.1914342    .0210737
               |
        region |
      Midwest  |  -.0218982   .0462112    -0.47   0.636    -.1125028    .0687063
        South  |  -.0230429   .0423656    -0.54   0.587    -.1061075    .0600218
         West  |  -.1302316   .0496828    -2.62   0.009    -.2276426   -.0328207
               |
          race |
        Black  |  -.0359667   .0524812    -0.69   0.493    -.1388644     .066931
     Hispanic  |   .1314881   .0591846     2.22   0.026     .0154474    .2475289
        Other  |  -.0859341   .0594922    -1.44   0.149    -.2025781    .0307098
               |
     education |  -.0003964    .018698    -0.02   0.983    -.0370567     .036264
         _cons |   2.327444   .1149369    20.25   0.000     2.102092    2.552796
--------------------------------------------------------------------------------

. svy: reg satisfaction internet age income female i.married i.region i.race ///
>         edu i.y 
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      8,596
Number of PSUs     =     8,596                  Population size   = 8,608.8684
                                                Design df         =      8,595
                                                F(  17,   8579)   =      12.17
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0406

--------------------------------------------------------------------------------
               |             Linearized
  satisfaction |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.2004253   .0236309    -8.48   0.000    -.2467475   -.1541031
           age |   .0044871   .0007484     6.00   0.000       .00302    .0059542
        income |   .0484645   .0112789     4.30   0.000     .0263551    .0705738
        female |  -.0090909    .021205    -0.43   0.668    -.0506577    .0324759
               |
       married |
Never married  |   .0326543   .0315526     1.03   0.301    -.0291965     .094505
     Divorced  |   .0068844   .0363386     0.19   0.850    -.0643481    .0781168
    Separated  |   .1269982   .0801116     1.59   0.113    -.0300398    .2840361
      Widowed  |   .0770631   .0480444     1.60   0.109    -.0171155    .1712416
     Partners  |  -.0101572   .0378868    -0.27   0.789    -.0844245      .06411
               |
        region |
      Midwest  |   -.033314   .0329331    -1.01   0.312    -.0978709    .0312428
        South  |  -.0579473   .0313958    -1.85   0.065    -.1194907    .0035961
         West  |  -.0830213   .0345228    -2.40   0.016    -.1506944   -.0153483
               |
          race |
        Black  |   .1744974   .0340356     5.13   0.000     .1077795    .2412154
     Hispanic  |   .2189719   .0354722     6.17   0.000     .1494379     .288506
        Other  |  -.0281051   .0470672    -0.60   0.550    -.1203681     .064158
               |
     education |   .0247722   .0135078     1.83   0.067    -.0017063    .0512507
           1.y |   .0044806   .0208285     0.22   0.830    -.0363483    .0453094
         _cons |   2.398288   .0752718    31.86   0.000     2.250737    2.545839
--------------------------------------------------------------------------------

. 
. svy: ologit satisfaction internet age income female i.married i.region edu ///
>         if year == 2012
(running ologit on estimation sample)

Survey: Ordered logistic regression

Number of strata   =         1                  Number of obs     =      5,191
Number of PSUs     =     5,191                  Population size   =  5,169.608
                                                Design df         =      5,190
                                                F(  13,   5178)   =       7.42
                                                Prob > F          =     0.0000

--------------------------------------------------------------------------------
               |             Linearized
  satisfaction |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.7139475   .0851812    -8.38   0.000    -.8809386   -.5469564
           age |   .0068293   .0025759     2.65   0.008     .0017795    .0118791
        income |   .0538455   .0383506     1.40   0.160    -.0213379    .1290289
        female |   .0321101   .0735632     0.44   0.662    -.1121048     .176325
               |
       married |
Never married  |   .2541196   .1026113     2.48   0.013     .0529584    .4552809
     Divorced  |   .1391958   .1231455     1.13   0.258    -.1022213     .380613
    Separated  |   .5080228   .2985942     1.70   0.089    -.0773476    1.093393
      Widowed  |   .3694001   .1721475     2.15   0.032     .0319185    .7068817
     Partners  |   .2120707    .137238     1.55   0.122    -.0569737     .481115
               |
        region |
      Midwest  |  -.1549802   .1163428    -1.33   0.183    -.3830611    .0731008
        South  |  -.0958717   .1121699    -0.85   0.393    -.3157719    .1240285
         West  |  -.0410443   .1183631    -0.35   0.729    -.2730858    .1909971
               |
     education |   .0406119   .0457587     0.89   0.375    -.0490945    .1303183
---------------+----------------------------------------------------------------
         /cut1 |   -2.40987    .253994    -9.49   0.000    -2.907805   -1.911935
         /cut2 |  -.5114128   .2441473    -2.09   0.036    -.9900443   -.0327814
         /cut3 |   2.310603   .2510248     9.20   0.000     1.818488    2.802717
--------------------------------------------------------------------------------

. margins , at(internet=(0 1)) post

Predictive margins                              Number of obs     =      5,191
Model VCE    : Linearized

1._predict   : Pr(satisfaction==1), predict(pr outcome(1))
2._predict   : Pr(satisfaction==2), predict(pr outcome(2))
3._predict   : Pr(satisfaction==3), predict(pr outcome(3))
4._predict   : Pr(satisfaction==4), predict(pr outcome(4))

1._at        : internet        =           0

2._at        : internet        =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_predict#_at |
        1 1  |   .0455391   .0047798     9.53   0.000     .0361686    .0549096
        1 2  |   .0886766   .0059133    15.00   0.000     .0770841    .1002692
        2 1  |   .1949613   .0106161    18.36   0.000     .1741493    .2157734
        2 2  |   .3029138   .0099917    30.32   0.000     .2833258    .3225017
        3 1  |   .5987513   .0106558    56.19   0.000     .5778614    .6196412
        3 2  |   .5224461   .0096689    54.03   0.000      .503491    .5414013
        4 1  |   .1607483    .010548    15.24   0.000     .1400697    .1814268
        4 2  |   .0859635   .0053866    15.96   0.000     .0754034    .0965235
------------------------------------------------------------------------------

. svy: ologit satisfaction internet age income female i.married i.region edu ///
>         if year == 2016
(running ologit on estimation sample)

Survey: Ordered logistic regression

Number of strata   =         1                  Number of obs     =      3,422
Number of PSUs     =     3,422                  Population size   =  3,452.447
                                                Design df         =      3,421
                                                F(  13,   3409)   =      10.03
                                                Prob > F          =     0.0000

--------------------------------------------------------------------------------
               |             Linearized
  satisfaction |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.4110178   .0974939    -4.22   0.000      -.60217   -.2198656
           age |   .0172694   .0029276     5.90   0.000     .0115294    .0230095
        income |   .1958001   .0469641     4.17   0.000     .1037195    .2878807
        female |  -.0968936   .0819044    -1.18   0.237    -.2574801     .063693
               |
       married |
Never married  |  -.0976279   .1318203    -0.74   0.459    -.3560825    .1608267
     Divorced  |  -.1238352   .1436593    -0.86   0.389    -.4055018    .1578314
    Separated  |   .3202675   .3923029     0.82   0.414    -.4489042    1.089439
      Widowed  |  -.0064439    .189915    -0.03   0.973    -.3788022    .3659144
     Partners  |  -.2486905    .146256    -1.70   0.089    -.5354484    .0380675
               |
        region |
      Midwest  |  -.0609272   .1299164    -0.47   0.639    -.3156488    .1937943
        South  |  -.0364656   .1192311    -0.31   0.760     -.270237    .1973058
         West  |  -.2939022   .1357968    -2.16   0.031    -.5601532   -.0276513
               |
     education |   -.023688   .0529964    -0.45   0.655    -.1275958    .0802198
---------------+----------------------------------------------------------------
         /cut1 |  -1.874674   .3210756    -5.84   0.000    -2.504193   -1.245155
         /cut2 |   .1196649   .3099823     0.39   0.699    -.4881042     .727434
         /cut3 |   3.169749   .3241446     9.78   0.000     2.534212    3.805285
--------------------------------------------------------------------------------

. margins , at(internet=(0 1)) post

Predictive margins                              Number of obs     =      3,422
Model VCE    : Linearized

1._predict   : Pr(satisfaction==1), predict(pr outcome(1))
2._predict   : Pr(satisfaction==2), predict(pr outcome(2))
3._predict   : Pr(satisfaction==3), predict(pr outcome(3))
4._predict   : Pr(satisfaction==4), predict(pr outcome(4))

1._at        : internet        =           0

2._at        : internet        =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_predict#_at |
        1 1  |   .0519461   .0056118     9.26   0.000     .0409432    .0629489
        1 2  |   .0759648   .0059238    12.82   0.000     .0643503    .0875793
        2 1  |   .2254284   .0134887    16.71   0.000     .1989817     .251875
        2 2  |   .2867556    .009972    28.76   0.000     .2672039    .3063073
        3 1  |   .6011819    .012422    48.40   0.000     .5768266    .6255372
        3 2  |   .5528128   .0105817    52.24   0.000     .5320657    .5735599
        4 1  |   .1214437   .0107401    11.31   0.000      .100386    .1425013
        4 2  |   .0844668   .0058553    14.43   0.000     .0729867     .095947
------------------------------------------------------------------------------

. svy: ologit satisfaction internet age income female i.married i.region i.race ///
>         edu i.y 
(running ologit on estimation sample)

Survey: Ordered logistic regression

Number of strata   =         1                  Number of obs     =      8,596
Number of PSUs     =     8,596                  Population size   = 8,608.8684
                                                Design df         =      8,595
                                                F(  17,   8579)   =      12.87
                                                Prob > F          =     0.0000

--------------------------------------------------------------------------------
               |             Linearized
  satisfaction |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.6027912   .0650681    -9.26   0.000    -.7303403   -.4752422
           age |   .0128195   .0019712     6.50   0.000     .0089554    .0166835
        income |     .12835   .0298895     4.29   0.000     .0697593    .1869406
        female |  -.0183052   .0552145    -0.33   0.740    -.1265388    .0899285
               |
       married |
Never married  |   .0753898   .0820677     0.92   0.358    -.0854827    .2362622
     Divorced  |   .0035784   .0945025     0.04   0.970    -.1816692     .188826
    Separated  |   .3327786   .2362097     1.41   0.159     -.130249    .7958063
      Widowed  |   .1781524   .1308995     1.36   0.174    -.0784421    .4347468
     Partners  |  -.0475924    .099708    -0.48   0.633    -.2430441    .1478593
               |
        region |
      Midwest  |  -.0954432   .0872263    -1.09   0.274    -.2664276    .0755412
        South  |   -.138619   .0831528    -1.67   0.096    -.3016185    .0243805
         West  |  -.2061648   .0909447    -2.27   0.023    -.3844383   -.0278914
               |
          race |
        Black  |   .4744327   .0937449     5.06   0.000     .2906703    .6581952
     Hispanic  |   .6004125   .0973696     6.17   0.000     .4095448    .7912803
        Other  |  -.0157256   .1199909    -0.13   0.896    -.2509366    .2194854
               |
     education |   .0534352   .0352763     1.51   0.130    -.0157147    .1225852
           1.y |   .0111726   .0545845     0.20   0.838    -.0958262    .1181713
---------------+----------------------------------------------------------------
         /cut1 |  -1.886888   .2017212    -9.35   0.000     -2.28231   -1.491466
         /cut2 |   .0477474   .1947302     0.25   0.806    -.3339705    .4294654
         /cut3 |   2.960195   .2017646    14.67   0.000     2.564688    3.355702
--------------------------------------------------------------------------------

. margins , at(internet=(0 1)) post

Predictive margins                              Number of obs     =      8,596
Model VCE    : Linearized

1._predict   : Pr(satisfaction==1), predict(pr outcome(1))
2._predict   : Pr(satisfaction==2), predict(pr outcome(2))
3._predict   : Pr(satisfaction==3), predict(pr outcome(3))
4._predict   : Pr(satisfaction==4), predict(pr outcome(4))

1._at        : internet        =           0

2._at        : internet        =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_predict#_at |
        1 1  |    .047561   .0036711    12.96   0.000     .0403648    .0547573
        1 2  |    .083321   .0042424    19.64   0.000     .0750049    .0916371
        2 1  |   .2051622   .0084188    24.37   0.000     .1886593    .2216651
        2 2  |   .2955017   .0071459    41.35   0.000     .2814941    .3095094
        3 1  |   .6010475   .0080743    74.44   0.000       .58522     .616875
        3 2  |   .5349458   .0071748    74.56   0.000     .5208815      .54901
        4 1  |   .1462293   .0077636    18.84   0.000     .1310108    .1614479
        4 2  |   .0862314   .0039958    21.58   0.000     .0783988    .0940641
------------------------------------------------------------------------------

.         
. ********************************************************
. * Adjusting for partisanhip, ideology, and vote choice *
. ********************************************************
. 
. svy: reg satis_binary internet age income female i.married i.region i.race ///
>         edu winner pid ideo if year == 2012     
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      3,573
Number of PSUs     =     3,573                  Population size   =  3,582.036
                                                Design df         =      3,572
                                                F(  19,   3554)   =       9.87
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0740

--------------------------------------------------------------------------------
               |             Linearized
  satis_binary |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.1474434   .0228112    -6.46   0.000    -.1921677   -.1027192
           age |   .0014437   .0007553     1.91   0.056    -.0000373    .0029246
        income |   .0331357   .0106667     3.11   0.002     .0122222    .0540492
        female |  -.0073499   .0209338    -0.35   0.726    -.0483933    .0336935
               |
       married |
Never married  |   .0274081    .031442     0.87   0.383     -.034238    .0890542
     Divorced  |   .0099991   .0330924     0.30   0.763    -.0548828    .0748809
    Separated  |   .0136419   .0639233     0.21   0.831    -.1116879    .1389716
      Widowed  |   .0685811   .0453786     1.51   0.131    -.0203896    .1575517
     Partners  |  -.0002472   .0395638    -0.01   0.995    -.0778172    .0773227
               |
        region |
      Midwest  |   .0016977   .0317238     0.05   0.957     -.060501    .0638963
        South  |  -.0227808   .0295814    -0.77   0.441     -.080779    .0352173
         West  |     .00477   .0325699     0.15   0.884    -.0590874    .0686275
               |
          race |
        Black  |   .0535191   .0350239     1.53   0.127    -.0151497     .122188
     Hispanic  |   .1314464   .0332252     3.96   0.000     .0663042    .1965886
        Other  |   .0413082   .0442579     0.93   0.351    -.0454651    .1280814
               |
     education |   .0186459   .0131712     1.42   0.157    -.0071778    .0444697
        winner |   .1249728   .0370213     3.38   0.001     .0523878    .1975578
           pid |  -.0191261   .0084345    -2.27   0.023    -.0356631   -.0025891
          ideo |   .0109284   .0092472     1.18   0.237    -.0072018    .0290586
         _cons |   .4873733   .0913956     5.33   0.000     .3081805    .6665661
--------------------------------------------------------------------------------

. svy: reg satis_binary internet age income female i.married i.region i.race ///
>         edu winner pid ideo if year == 2016     
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      2,064
Number of PSUs     =     2,064                  Population size   =  2,009.967
                                                Design df         =      2,063
                                                F(  19,   2045)   =       7.66
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0902

--------------------------------------------------------------------------------
               |             Linearized
  satis_binary |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.0838873   .0262367    -3.20   0.001    -.1353405   -.0324341
           age |   .0036714   .0007785     4.72   0.000     .0021447    .0051981
        income |    .048334   .0132255     3.65   0.000     .0223974    .0742707
        female |   .0451659   .0234262     1.93   0.054    -.0007755    .0911073
               |
       married |
Never married  |  -.0467445   .0402741    -1.16   0.246    -.1257267    .0322377
     Divorced  |   .0182142   .0383937     0.47   0.635    -.0570802    .0935087
    Separated  |   -.113977   .1126965    -1.01   0.312    -.3349876    .1070337
      Widowed  |   .0225245   .0507807     0.44   0.657    -.0770624    .1221113
     Partners  |  -.0941473   .0439922    -2.14   0.032     -.180421   -.0078736
               |
        region |
      Midwest  |   .0229252    .038014     0.60   0.547    -.0516245    .0974749
        South  |   .0061001   .0353466     0.17   0.863    -.0632186    .0754189
         West  |  -.0265988   .0388166    -0.69   0.493    -.1027225     .049525
               |
          race |
        Black  |  -.0342241   .0490394    -0.70   0.485    -.1303959    .0619477
     Hispanic  |   .0641606   .0480142     1.34   0.182    -.0300007    .1583219
        Other  |  -.0350137    .049744    -0.70   0.482    -.1325675      .06254
               |
     education |  -.0029379   .0156604    -0.19   0.851    -.0336497     .027774
        winner |  -.0544104   .0413269    -1.32   0.188    -.1354572    .0266364
           pid |   .0075132   .0089336     0.84   0.400    -.0100066    .0250331
          ideo |   .0455172    .011445     3.98   0.000     .0230723    .0679622
         _cons |   .2316533   .0994148     2.33   0.020     .0366895    .4266172
--------------------------------------------------------------------------------

. svy: reg satis_binary internet age income female i.married i.region i.race ///
>         edu winner pid ideo i.y
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      5,637
Number of PSUs     =     5,637                  Population size   =  5,592.003
                                                Design df         =      5,636
                                                F(  20,   5617)   =      12.77
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0659

--------------------------------------------------------------------------------
               |             Linearized
  satis_binary |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.1204051    .017682    -6.81   0.000    -.1550686   -.0857416
           age |   .0022959   .0005658     4.06   0.000     .0011867    .0034051
        income |   .0388286   .0084017     4.62   0.000      .022358    .0552992
        female |   .0098038   .0160166     0.61   0.540     -.021595    .0412025
               |
       married |
Never married  |   .0022327   .0251715     0.09   0.929    -.0471132    .0515786
     Divorced  |   .0088132   .0260718     0.34   0.735    -.0422975    .0599239
    Separated  |  -.0285054   .0563604    -0.51   0.613    -.1389936    .0819828
      Widowed  |   .0492324   .0351666     1.40   0.162    -.0197077    .1181726
     Partners  |  -.0421485   .0302518    -1.39   0.164    -.1014538    .0171567
               |
        region |
      Midwest  |   .0096622   .0247127     0.39   0.696    -.0387843    .0581086
        South  |  -.0144164   .0230761    -0.62   0.532    -.0596545    .0308217
         West  |  -.0062188    .025117    -0.25   0.804    -.0554578    .0430201
               |
          race |
        Black  |   .0392665    .028781     1.36   0.173    -.0171553    .0956883
     Hispanic  |   .1217871   .0276962     4.40   0.000     .0674919    .1760823
        Other  |   .0123038   .0337343     0.36   0.715    -.0538284    .0784361
               |
     education |   .0145012   .0104092     1.39   0.164    -.0059047    .0349072
        winner |   .1541546   .0158629     9.72   0.000     .1230571     .185252
           pid |  -.0153468   .0050215    -3.06   0.002    -.0251909   -.0055026
          ideo |   .0194834   .0070288     2.77   0.006     .0057043    .0332624
           1.y |   .0375252   .0158885     2.36   0.018     .0063777    .0686727
         _cons |   .3575582    .062621     5.71   0.000     .2347968    .4803195
--------------------------------------------------------------------------------

. 
.         
. ********************************************************************************
. *                                   Extensions                                 *
. ********************************************************************************
. 
. *******************
. * Alternative DVs *
. *******************
. gen power = VCF9253

. recode power (-9=.) (-8=.)
(power: 1087 changes made)

. gen difference_vote = VCF9250

. recode difference_vote (-9=.) (-8=.)
(difference_vote: 1074 changes made)

. 
. gen trust = VCF0605

. recode trust (1=0) (2=1) (0=.) (9=.)
(trust: 10184 changes made)

. label define trustlabel 0 "0. A few big interests" 1 "1. Benefit of all"

. label values trust trustlabel  

. 
. alpha power difference , gen(efficacy)

Test scale = mean(unstandardized items)

Average interitem covariance:     .6866191
Number of items in the scale:            2
Scale reliability coefficient:      0.6795

.         replace efficacy = . if power == .
(21 real changes made, 21 to missing)

.         replace efficacy = . if difference == .
(8 real changes made, 8 to missing)

. 
. svy: reg trust internet age income female i.married i.region i.race ///
>         edu if year == 2012
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      5,477
Number of PSUs     =     5,477                  Population size   = 5,471.1774
                                                Design df         =      5,476
                                                F(  16,   5461)   =       9.95
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0475

--------------------------------------------------------------------------------
               |             Linearized
         trust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.0874183   .0163707    -5.34   0.000    -.1195114   -.0553253
           age |  -.0007187   .0005128    -1.40   0.161     -.001724    .0002866
        income |  -.0002781    .008096    -0.03   0.973    -.0161494    .0155932
        female |   .0525439   .0145739     3.61   0.000     .0239733    .0811145
               |
       married |
Never married  |   .0057253    .021811     0.26   0.793    -.0370329    .0484835
     Divorced  |   .0519856    .026572     1.96   0.050    -.0001062    .1040773
    Separated  |   .0455136    .062307     0.73   0.465    -.0766328    .1676601
      Widowed  |  -.0395877   .0274985    -1.44   0.150    -.0934957    .0143203
     Partners  |  -.0196799   .0264844    -0.74   0.457    -.0715999    .0322401
               |
        region |
      Midwest  |   -.034205   .0241821    -1.41   0.157    -.0816115    .0132015
        South  |  -.0581379   .0221398    -2.63   0.009    -.1015407    -.014735
         West  |  -.0498616    .024198    -2.06   0.039    -.0972993    -.002424
               |
          race |
        Black  |   .1401554   .0256121     5.47   0.000     .0899455    .1903654
     Hispanic  |   .1590158   .0263874     6.03   0.000     .1072859    .2107456
        Other  |   .0275575   .0325347     0.85   0.397    -.0362234    .0913383
               |
     education |  -.0138165   .0093875    -1.47   0.141    -.0322198    .0045868
         _cons |   .3054049   .0500914     6.10   0.000     .2072058     .403604
--------------------------------------------------------------------------------

. svy: reg trust internet age income female i.married i.region i.race ///
>         edu if year == 2016
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      3,959
Number of PSUs     =     3,959                  Population size   =  4,000.686
                                                Design df         =      3,958
                                                F(  16,   3943)   =       7.35
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0567

--------------------------------------------------------------------------------
               |             Linearized
         trust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.0361185   .0165673    -2.18   0.029    -.0685998   -.0036372
           age |  -.0007317   .0004736    -1.54   0.122    -.0016602    .0001969
        income |  -.0296135   .0075065    -3.95   0.000    -.0443304   -.0148967
        female |  -.0007147   .0144392    -0.05   0.961    -.0290236    .0275942
               |
       married |
Never married  |  -.0013912   .0227747    -0.06   0.951    -.0460425      .04326
     Divorced  |  -.0110701    .024285    -0.46   0.649    -.0586823    .0365421
    Separated  |   .0087492   .0629574     0.14   0.889    -.1146828    .1321812
      Widowed  |    .012352   .0378975     0.33   0.744    -.0619484    .0866524
     Partners  |  -.0308803   .0251922    -1.23   0.220    -.0802711    .0185105
               |
        region |
      Midwest  |  -.0474116   .0218376    -2.17   0.030    -.0902256   -.0045975
        South  |  -.0115576   .0213271    -0.54   0.588    -.0533708    .0302556
         West  |   .0002482   .0249397     0.01   0.992    -.0486478    .0491441
               |
          race |
        Black  |   .1558443   .0306291     5.09   0.000      .095794    .2158947
     Hispanic  |    .178261    .029905     5.96   0.000     .1196303    .2368917
        Other  |   .0436203   .0255457     1.71   0.088    -.0064637    .0937043
               |
     education |  -.0024162   .0088419    -0.27   0.785    -.0197514    .0149189
         _cons |    .305888   .0515373     5.94   0.000     .2048458    .4069302
--------------------------------------------------------------------------------

. svy: reg trust internet age income female i.married i.region i.race ///
>         edu i.y
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      9,436
Number of PSUs     =     9,436                  Population size   = 9,471.8634
                                                Design df         =      9,435
                                                F(  17,   9419)   =      15.16
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0468

--------------------------------------------------------------------------------
               |             Linearized
         trust |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.0681472       .012    -5.68   0.000    -.0916698   -.0446245
           age |  -.0007556   .0003594    -2.10   0.036    -.0014601   -.0000511
        income |  -.0121146   .0056889    -2.13   0.033    -.0232661   -.0009632
        female |   .0311618   .0104744     2.98   0.003     .0106296    .0516939
               |
       married |
Never married  |     .00239   .0159998     0.15   0.881    -.0289731    .0337531
     Divorced  |   .0266164    .018821     1.41   0.157    -.0102768    .0635095
    Separated  |   .0329142   .0456464     0.72   0.471    -.0565625     .122391
      Widowed  |  -.0167707   .0222354    -0.75   0.451    -.0603569    .0268156
     Partners  |  -.0232216   .0184796    -1.26   0.209    -.0594456    .0130023
               |
        region |
      Midwest  |  -.0403597   .0168542    -2.39   0.017    -.0733976   -.0073218
        South  |  -.0393768   .0157781    -2.50   0.013    -.0703054   -.0084483
         West  |  -.0293316   .0175796    -1.67   0.095    -.0637914    .0051283
               |
          race |
        Black  |   .1462626   .0197627     7.40   0.000     .1075233    .1850018
     Hispanic  |   .1663184   .0199011     8.36   0.000     .1273079    .2053288
        Other  |   .0351252   .0207457     1.69   0.090    -.0055409    .0757913
               |
     education |  -.0100613   .0065987    -1.52   0.127    -.0229961    .0028735
           1.y |  -.0190724   .0101755    -1.87   0.061    -.0390186    .0008738
         _cons |   .3187204   .0366201     8.70   0.000     .2469371    .3905038
--------------------------------------------------------------------------------

.         
. svy: reg efficacy internet age income female i.married i.region i.race ///
>         edu if year == 2012
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      5,207
Number of PSUs     =     5,207                  Population size   = 5,184.1768
                                                Design df         =      5,206
                                                F(  16,   5191)   =      14.28
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0652

--------------------------------------------------------------------------------
               |             Linearized
      efficacy |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.3333485    .038168    -8.73   0.000    -.4081737   -.2585233
           age |   .0095794   .0013203     7.26   0.000     .0069911    .0121677
        income |   .0382498   .0196429     1.95   0.052    -.0002586    .0767582
        female |   .1302933   .0382612     3.41   0.001     .0552854    .2053013
               |
       married |
Never married  |    .052467   .0585453     0.90   0.370    -.0623064    .1672405
     Divorced  |   .0089264   .0641567     0.14   0.889    -.1168476    .1347005
    Separated  |   .2051013   .1027382     2.00   0.046     .0036913    .4065112
      Widowed  |  -.0436983   .0855868    -0.51   0.610    -.2114844    .1240878
     Partners  |  -.1290395   .0737253    -1.75   0.080    -.2735719     .015493
               |
        region |
      Midwest  |  -.0601122   .0577696    -1.04   0.298     -.173365    .0531405
        South  |  -.0260033   .0532673    -0.49   0.625    -.1304296    .0784231
         West  |  -.0499537   .0601175    -0.83   0.406    -.1678092    .0679019
               |
          race |
        Black  |   .3901697   .0617126     6.32   0.000     .2691871    .5111523
     Hispanic  |   .1966099    .061761     3.18   0.001     .0755323    .3176875
        Other  |  -.0435899   .0970936    -0.45   0.653     -.233934    .1467542
               |
     education |    .067302   .0242044     2.78   0.005     .0198512    .1147528
         _cons |   3.126679   .1242578    25.16   0.000     2.883082    3.370277
--------------------------------------------------------------------------------

. svy: reg efficacy internet age income female i.married i.region i.race ///
>         edu if year == 2016
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      3,425
Number of PSUs     =     3,425                  Population size   =  3,449.289
                                                Design df         =      3,424
                                                F(  16,   3409)   =       9.35
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0616

--------------------------------------------------------------------------------
               |             Linearized
      efficacy |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.1158125   .0434505    -2.67   0.008    -.2010041    -.030621
           age |   .0098044    .001263     7.76   0.000     .0073282    .0122807
        income |   .0906412   .0199549     4.54   0.000     .0515165    .1297659
        female |   .1614243   .0396028     4.08   0.000     .0837768    .2390719
               |
       married |
Never married  |   .1063203    .061061     1.74   0.082    -.0133994      .22604
     Divorced  |  -.0745162   .0618749    -1.20   0.229    -.1958316    .0467992
    Separated  |   .2044065   .1561588     1.31   0.191    -.1017673    .5105803
      Widowed  |  -.0989476   .0985481    -1.00   0.315    -.2921667    .0942715
     Partners  |  -.0315735   .0691523    -0.46   0.648    -.1671573    .1040104
               |
        region |
      Midwest  |  -.0519603   .0630245    -0.82   0.410    -.1755298    .0716092
        South  |   .0243191   .0560234     0.43   0.664    -.0855235    .1341618
         West  |   .0528439   .0633684     0.83   0.404    -.0713998    .1770875
               |
          race |
        Black  |  -.0331124   .0729479    -0.45   0.650    -.1761382    .1099134
     Hispanic  |  -.0595622   .0706456    -0.84   0.399    -.1980739    .0789494
        Other  |  -.1115123   .0851652    -1.31   0.190    -.2784921    .0554675
               |
     education |   .0612189   .0244379     2.51   0.012     .0133045    .1091333
         _cons |   3.043934   .1390496    21.89   0.000     2.771305    3.316562
--------------------------------------------------------------------------------

. svy: reg efficacy internet age income female i.married i.region i.race ///
>         edu i.y
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      8,632
Number of PSUs     =     8,632                  Population size   = 8,633.4658
                                                Design df         =      8,631
                                                F(  17,   8615)   =      20.14
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0626

--------------------------------------------------------------------------------
               |             Linearized
      efficacy |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
      internet |  -.2510214   .0290896    -8.63   0.000    -.3080439   -.1939989
           age |   .0095949   .0009495    10.11   0.000     .0077336    .0114561
        income |   .0601945   .0143303     4.20   0.000     .0321038    .0882852
        female |   .1415708   .0281078     5.04   0.000     .0864728    .1966689
               |
       married |
Never married  |   .0741771   .0433032     1.71   0.087    -.0107075    .1590617
     Divorced  |  -.0179982   .0466554    -0.39   0.700    -.1094539    .0734575
    Separated  |   .2060361   .0859697     2.40   0.017      .037515    .3745572
      Widowed  |  -.0673133   .0647618    -1.04   0.299    -.1942618    .0596353
     Partners  |  -.0876968   .0519059    -1.69   0.091    -.1894447    .0140512
               |
        region |
      Midwest  |   -.056021   .0428819    -1.31   0.191    -.1400797    .0280377
        South  |  -.0033746   .0389523    -0.09   0.931    -.0797303    .0729812
         West  |  -.0100186   .0441741    -0.23   0.821    -.0966103    .0765732
               |
          race |
        Black  |   .2222501   .0475031     4.68   0.000     .1291327    .3153675
     Hispanic  |   .0918698   .0468298     1.96   0.050     .0000723    .1836674
        Other  |  -.0698011   .0662549    -1.05   0.292    -.1996764    .0600743
               |
     education |   .0635332   .0177157     3.59   0.000     .0288061    .0982603
           1.y |     .20565   .0275177     7.47   0.000     .1517087    .2595913
         _cons |   3.023284   .0940051    32.16   0.000     2.839012    3.207557
--------------------------------------------------------------------------------

. 
. **********************************************************************
. * "Traditional model" with interaction between winner/loser and mode *
. **********************************************************************
. svy: reg satis_binary interest natl_econ ideo income age educ female ///
>         winner##internet if year == 2012        
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      3,582
Number of PSUs     =     3,582                  Population size   = 3,587.0542
                                                Design df         =      3,581
                                                F(  10,   3572)   =      18.40
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0745

---------------------------------------------------------------------------------
                |             Linearized
   satis_binary |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
       interest |  -.0348365   .0160162    -2.18   0.030    -.0662382   -.0034348
      natl_econ |   .0516903   .0112443     4.60   0.000     .0296443    .0737362
           ideo |   .0089133   .0088225     1.01   0.312    -.0083844     .026211
         income |   .0203459   .0101103     2.01   0.044     .0005234    .0401685
            age |   .0015656   .0006596     2.37   0.018     .0002722    .0028589
      education |   .0065998   .0130607     0.51   0.613    -.0190073    .0322069
         female |  -.0065768   .0205531    -0.32   0.749    -.0468738    .0337201
                |
         winner |
        Winner  |   .1305652   .0443728     2.94   0.003     .0435667    .2175638
     1.internet |   -.152982   .0378929    -4.04   0.000    -.2272758   -.0786882
                |
winner#internet |
      Winner#1  |    .016317    .046226     0.35   0.724    -.0743149    .1069488
                |
          _cons |   .4476371   .0883589     5.07   0.000     .2743984    .6208759
---------------------------------------------------------------------------------

. svy: reg satis_binary interest natl_econ ideo income age educ female ///
>         winner##internet if year == 2016
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      2,072
Number of PSUs     =     2,072                  Population size   =  2,014.647
                                                Design df         =      2,071
                                                F(  10,   2062)   =      15.58
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0907

---------------------------------------------------------------------------------
                |             Linearized
   satis_binary |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
       interest |   .0154708   .0212712     0.73   0.467    -.0262443    .0571859
      natl_econ |   .0472182    .012377     3.81   0.000     .0229455    .0714908
           ideo |   .0553017   .0100291     5.51   0.000     .0356335    .0749698
         income |    .053034   .0119898     4.42   0.000     .0295206    .0765474
            age |   .0042395   .0007199     5.89   0.000     .0028278    .0056512
      education |  -.0049313   .0156613    -0.31   0.753    -.0356449    .0257823
         female |    .051016   .0233193     2.19   0.029     .0052841    .0967478
                |
         winner |
        Winner  |   .0488284   .0508558     0.96   0.337    -.0509054    .1485622
     1.internet |  -.0567439   .0397336    -1.43   0.153     -.134666    .0211781
                |
winner#internet |
      Winner#1  |  -.0405866   .0514546    -0.79   0.430    -.1414947    .0603215
                |
          _cons |  -.0699956   .1086764    -0.64   0.520    -.2831221    .1431309
---------------------------------------------------------------------------------

. svy: reg satis_binary interest natl_econ ideo income age educ female ///
>         winner##internet i.y
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      5,654
Number of PSUs     =     5,654                  Population size   = 5,601.7012
                                                Design df         =      5,653
                                                F(  11,   5643)   =      25.10
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0694

---------------------------------------------------------------------------------
                |             Linearized
   satis_binary |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
       interest |  -.0164697   .0130831    -1.26   0.208    -.0421176    .0091783
      natl_econ |   .0569077   .0078996     7.20   0.000     .0414214     .072394
           ideo |   .0196252   .0055167     3.56   0.000     .0088103    .0304401
         income |   .0306056   .0078352     3.91   0.000     .0152455    .0459656
            age |   .0025535   .0005002     5.10   0.000     .0015728    .0035341
      education |   .0026581   .0103022     0.26   0.796    -.0175382    .0228544
         female |   .0133496   .0158172     0.84   0.399    -.0176582    .0443574
                |
         winner |
        Winner  |   .1580954   .0302062     5.23   0.000     .0988795    .2173112
     1.internet |  -.1084044    .028478    -3.81   0.000    -.1642322   -.0525766
                |
winner#internet |
      Winner#1  |  -.0153932   .0352739    -0.44   0.663    -.0845436    .0537573
                |
            1.y |   .0272618   .0159808     1.71   0.088    -.0040668    .0585904
          _cons |   .2312235   .0676749     3.42   0.001     .0985547    .3638924
---------------------------------------------------------------------------------

. 
. use "/Users/jrthornton/Desktop/post_age_balance.dta"    , replace

. # delimit ;     
delimiter now ;
.         twoway 
>         (line p age , color(black)) (
>         line lower age , lcolor(black) lpattern(dash)) 
>         (line upper age , lcolor(black) lpattern(dash)) 
>         , 
>         legend(off)
>         aspect(1) 
>         ylab(.5 "0.5" .6 "0.6" .7 "0.7" .8 "0.8" .9 "0.9") 
>         xlab(20(10)90)
>         xtitle("Age") 
>         ytitle("Pr(Internet)")
>         ;

. # delimit cr
delimiter now cr
. 
end of do-file

. log close
      name:  <unnamed>
       log:  /Users/jrthornton/Desktop/replication.log
  log type:  text
 closed on:  30 Jun 2023, 13:36:49
--------------------------------------------------------------------------------------------------------------------
